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run-local-model-lab

Compare local MLX models against frontier gateways on latency, cost, and quality.

10|5|Updated Jun 3, 2026
Search Tags:#evaluation#mlx#ml#arena#local-model#frontier
One-click install
npx skills add https://github.com/understudylabs/understudy-agent-tools --skill run-local-model-lab
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: run-local-model-lab
Source: https://github.com/understudylabs/understudy-agent-tools/tree/main/skills/run-local-model-lab
Command: npx skills add https://github.com/understudylabs/understudy-agent-tools --skill run-local-model-lab

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires tmux, curl, lsof, node, pi, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This skill helps developers quickly evaluate local MLX models against frontier gateways to determine the best route for a workload, enabling private, low-cost inference on Apple Silicon while comparing against hosted options.

Core Features & Use Cases

  • Side-by-side local-vs-frontier evaluation: serve two MLX models (local and frontier-backed) to compare latency, cost, and quality on the same workload.
  • Hardware & runtime gating: verify Apple Silicon availability, unified memory, and the correct MLX runtime before running evaluations.
  • Route decision traceability: produce a clear decision output detailing which path won, model tier used, and artifacts/logs for reproducibility.

Quick Start

Start the two-server arena to run a side-by-side local MLX model and a frontier gateway for head-to-head comparison.

Frequently Asked Questions about run-local-model-lab

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I evaluate local MLX models against frontier gateways on Apple Silicon?▼

The best way to compare local MLX serving against frontier gateways is running a side-by-side arena that measures latency, cost, and quality to produce a structured route decision for your workload.

Can I serve local MLX models using an OpenAI-compatible endpoint?▼

Yes, you can serve local MLX models using OpenAI-compatible endpoints via mlx_lm.server and mlx_vlm.server, enabling direct comparison against frontier deployments on Apple Silicon.

What hardware do I need to run a local MLX evaluation lab?▼

Running a local MLX evaluation lab requires Apple Silicon hardware with sufficient unified memory, verified through hardware checks and MLX runtime gating before serving local models.

Does this workflow support both text and vision language models for MLX?▼

Yes, the workflow supports serving both text and vision language models locally via mlx_lm.server and mlx_vlm.server, allowing comprehensive evaluation against frontier gateway alternatives.

How do I decide the best inference route between local MLX and frontier gateways?▼

You decide the best inference route by evaluating multiple candidate models across latency, cost, and quality, producing a structured output detailing the chosen route, model tier, and artifacts.

Do I need Node and tmux to run a local MLX model arena?▼

Yes, running the local MLX model arena requires dependencies including Node, tmux, curl, lsof, and pi to manage the side-by-side server evaluation workflow and enforce the guided arena setup.

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